Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer.
Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer.
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DOI：
10.1186/1559-0275-8-11
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发表时间：
2011-07-08
影响因子：
3.8
通讯作者：
Mills GB
中科院分区：
文献类型：
--
作者：
Gonzalez-Angulo AM;Hennessy BT;Meric-Bernstam F;Sahin A;Liu W;Ju Z;Carey MS;Myhre S;Speers C;Deng L;Broaddus R;Lluch A;Aparicio S;Brown P;Pusztai L;Symmans WF;Alsner J;Overgaard J;Borresen-Dale AL;Hortobagyi GN;Coombes KR;Mills GB
确定功能蛋白质组学是否可以改善乳腺癌的分类和诊断，并预测接受新辅助紫杉烷和蒽环类紫杉烷为基础的系统治疗（NST）的患者的病理完全缓解（pCR）。将使用146种与乳腺癌相关的蛋白质的抗体的反相蛋白质阵列（RPPA）应用于三个独立的肿瘤集合。在两个原发性乳腺癌患者队列的测试集（n = 168）上验证了用于识别来自训练集（n = 712）的手术切除标本中的亚组和预后的监督聚类。使用有序逻辑回归构建评分，以量化训练集中的复发概率，并在测试集中进行测试。然后对132例接受NST治疗的患者的FNA活检进行评分，以确定预测pCR的能力。在712个肿瘤训练集中，通过10种蛋白质生物标志物组鉴定了6个乳腺癌亚组。它们与不同的无复发生存期（RFS）相关（对数秩p = 8.8 E-10）。在检验集中证实了6个亚组预测RFS的结构和能力（对数秩p = 0.0013）。使用训练集中的10种蛋白质构建的预后评分与训练集和测试集中的RFS相关（对于测试集，p = 3.2E-13）。在FNA集中，预后评分与pCR至NST的可能性之间存在显著相关性（p = 0.0021）。我们开发了一个10种蛋白质生物标志物面板，将乳腺癌分为预后组，可能对接受蒽环类紫杉烷NST的患者的管理具有潜在的实用性。
To determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and anthracycline-taxane-based systemic therapy (NST). Reverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR. Six breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021). We developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST.